[fxtentacle]

Sadly, I believe you will be proven correct.

What that neural network learns is basically an approximation of a static impulse response. So while it can simulate linear time-invariant effects such as reverb quite nicely, it'll surely have issues with chorus.

[sk0g]

Reverb is time invariant? You can set custom decay time, rate etc, so the one not can be heard for say, 10 seconds if you want to go full Devin Townsend. I'd think Chorus would work better.

I wanted to do a very similar project, but with an overdrive. Let's see if I get time anytime soon!

[InitialLastName]

>Reverb is time invariant?

You might want to familiarize yourself with [0]. Time-invariance is a specific property of a system, where the output (for any given input) has no dependency on if the input signal happens now or 1 second from now or 100 years from now (except for the corresponding delay). Most reverb models are, to a first approximation, time invariant, because the effect will have the same sound for the same guitar line, no matter when you play the line.

Chorus, on the other hand, has a (perhaps subtle) modulator to get that warbly (scientific word!) sound. It doesn't feel like a time-based effect, but it certainly is and that makes it quite a lot more difficult to mimic with a system that (as others have noted) boils down to an impulse response.

[0]https://en.wikipedia.org/wiki/Time-invariant_system

[TheOtherHobbes]

Impulse response reverbs (Bricasti, etc) are based on time-invariant convolution.

Studio reverbs famously aren't, and some of the most popular models (notably Lexicon) have included time-variant algorithms since the late 70s. The processing power to handle IR convolution didn't exist, and it turned out some time variation added lushness and density to the sound that simpler models couldn't capture.

Modelling a chorus or time-variant reverb with any form of convolution - including any convolution-based neural net - is a complete waste of time, because most chorus algos are trivial and convolution is completely the wrong tool for the job.

It's literally about as useful as taking a still picture of a 90 minute movie.

[sk0g]

Thanks for the info! Did some reading and I can see plain reverb being time invariant indeed :) I never realised chorus pedals did more than just stack frequency offsets onto your signal, but I only really play distorted so choruses are of limited use to me.

Pasting the other response below as well:

> Ah righto, the reverb pedal I'm most familiar with turns out to not be just reverb - EQD Afterneath does a whole bunch of funky stuff. Plain reverb though, yeah. I was approaching this more from the angle of training a neural network, where the input and output waves have to be correlated over a great span of time/ samples.

[vnorilo]

Reverb is indeed linear time invariant (sans some rarer internal modulation techniques) but it's quite a high order filter.

[sk0g]

Ah righto, the reverb pedal I'm most familiar with turns out to not be just reverb - EQD Afterneath does a whole bunch of funky stuff. Plain reverb though, yeah. I was approaching this more from the angle of training a neural network, where the input and output waves have to be correlated over a great span of time/ samples.

[luckydata]

Then what you need is a multi dimensional matrix of impulse responses, one for each combination of parameter. You can further simplify the model by limiting to only useful combination ranges etc...

[ssalazar]

> What that neural network learns is basically an approximation of a static impulse response.

Im curious, do you have a reference or source for this? Distortion is non-linear making it impractical to model using an impulse response. Is there something about neural networks that makes them good for modeling non-linear but time-invariant effects?